Oxytocin in Functional Neurological Disorders
Samantha Weber
Department of Neurology, Psychosomatic Medicine Unit, University Hospital Inselspital, 3010 Bern, Switzerlandsamantha.weber@bli.uzh.ch
03/09/24
1 Introduction
We collected saliva samples before and after entering the scanner. Those samples were then poured together for OT analysis.
## [1] "Number of subjects with OT Data at T1: "##
## FND HC
## 59 65## [1] " "## [1] "Mean Age at T1: "## # A tibble: 2 × 5
## group variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 FND age 59 38.4 14.1
## 2 HC age 65 33.2 11.2## [1] "Significantly different: "## [1] 0.02560641.1 Oxytocin FND vs. HC
We first visualize the data.
## [1] FALSE
1.1.1 Statistical Analysis OT FND vs. HC
I applied an ANOVA on the fitted data (using lm), so that we correct/include covariates, which are:
## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 18.4 18.37 1.330 0.251
## gender 1 14.5 14.48 1.048 0.308
## psychMed 1 1.8 1.83 0.132 0.717
## bdi 1 0.1 0.12 0.009 0.926
## stai1 1 0.0 0.02 0.002 0.966
## date_diff 1 34.0 34.02 2.463 0.119
## contraception 2 18.7 9.37 0.679 0.509
## age 1 8.8 8.80 0.637 0.427
## Residuals 114 1574.5 13.81There is no significant group difference in FND vs. HC. Also, covariates do not seem to affect the results strongly.
2 Genetic association study
Previously, we saw that rs53576 was significantly associated to the diagnosis of FND (= risk factor). We now first replicate this in the subset of our sample.
We start with quality control.
- Hardy Weinberg Equilibrium
## alleles major.allele.freq HWE missing (%)
## rs1360780 C/T 65.7 0.230253 0
## rs1491850 T/C 63.3 0.698607 0
## rs1799732 G/I 89.5 0.358274 0
## rs1800532 G/T 57.7 0.042245 0
## rs2254298 G/A 86.3 1.000000 0
## rs3758653 T/C 81.0 1.000000 0
## rs3800373 A/C 70.2 0.131083 0
## rs4570625 G/T 77.8 0.794373 0
## rs53576 G/A 60.9 0.850544 0
## rs6265 C/T 83.1 0.750615 0All fine, except of rs1800532.
Now we run the association study. These are our genes:We first give an overview over all p-values.
## comments codominant dominant recessive log-additive
## rs1360780 - 0.97963 0.97297 0.84020 0.90533
## rs1491850 - 0.61674 0.99334 0.36466 0.64470
## rs1799732 - 0.80801 - - -
## rs1800532 - 0.54478 0.46094 0.28771 0.29488
## rs2254298 - 0.23474 0.11969 0.66313 0.17044
## rs3758653 - 0.85104 0.84949 0.57143 0.72493
## rs3800373 - 0.71642 0.90302 0.41608 0.66630
## rs4570625 - 0.80468 0.51253 0.91672 0.55731
## rs53576 - 0.04556 0.69019 0.02845 0.37433
## rs6265 - 0.50057 0.26657 0.98025 0.36224## $rs1360780
##
## SNP: rs1360780 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## C/C 26 40.0 24 40.7 1.00 0.9796 140.7
## C/T 34 52.3 29 49.2 1.01 0.40 2.57
## T/T 5 7.7 6 10.2 0.86 0.18 4.18
## Dominant
## C/C 26 40.0 24 40.7 1.00 0.9730 138.8
## C/T-T/T 39 60.0 35 59.3 0.98 0.40 2.41
## Recessive
## C/C-C/T 60 92.3 53 89.8 1.00 0.8402 138.7
## T/T 5 7.7 6 10.2 0.86 0.19 3.83
## log-Additive
## 0,1,2 65 52.4 59 47.6 0.96 0.48 1.91 0.9053 138.7
##
## $rs1491850
##
## SNP: rs1491850 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## T/T 26 40.0 25 42.4 1.00 0.6167 139.8
## C/T 28 43.1 27 45.8 1.21 0.45 3.21
## C/C 11 16.9 7 11.9 0.63 0.17 2.32
## Dominant
## T/T 26 40.0 25 42.4 1.00 0.9933 138.8
## C/T-C/C 39 60.0 34 57.6 1.00 0.41 2.47
## Recessive
## T/T-C/T 54 83.1 52 88.1 1.00 0.3647 137.9
## C/C 11 16.9 7 11.9 0.57 0.17 1.93
## log-Additive
## 0,1,2 65 52.4 59 47.6 0.87 0.47 1.60 0.6447 138.5
##
## $rs1799732
##
## SNP: rs1799732 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## G/G 51 78.5 47 79.7 1.00 0.808 138.7
## G/I 14 21.5 12 20.3 0.87 0.3 2.58
## log-Additive
## 0,1,2 65 52.4 59 47.6 0.87 0.3 2.58 138.7
##
## $rs1800532
##
## SNP: rs1800532 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## G/G 28 43.1 19 32.2 1.00 0.5448 139.5
## G/T 25 38.5 24 40.7 1.16 0.42 3.25
## T/T 12 18.5 16 27.1 1.87 0.60 5.84
## Dominant
## G/G 28 43.1 19 32.2 1.00 0.4609 138.2
## G/T-T/T 37 56.9 40 67.8 1.41 0.57 3.51
## Recessive
## G/G-G/T 53 81.5 43 72.9 1.00 0.2877 137.6
## T/T 12 18.5 16 27.1 1.73 0.63 4.79
## log-Additive
## 0,1,2 65 52.4 59 47.6 1.35 0.77 2.38 0.2949 137.7
##
## $rs2254298
##
## SNP: rs2254298 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## G/G 49 75.4 43 72.9 1.00 0.2347 137.9
## G/A 15 23.1 15 25.4 2.41 0.83 6.95
## A/A 1 1.5 1 1.7 0.40 0.00 89.54
## Dominant
## G/G 49 75.4 43 72.9 1.00 0.1197 136.3
## G/A-A/A 16 24.6 16 27.1 2.27 0.80 6.45
## Recessive
## G/G-G/A 64 98.5 58 98.3 1.00 0.6631 138.6
## A/A 1 1.5 1 1.7 0.33 0.00 58.97
## log-Additive
## 0,1,2 65 52.4 59 47.6 1.96 0.75 5.14 0.1704 136.9
##
## $rs3758653
##
## SNP: rs3758653 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## T/T 42 64.6 39 66.1 1.00 0.8510 140.4
## T/C 20 30.8 19 32.2 0.98 0.37 2.60
## C/C 3 4.6 1 1.7 0.48 0.04 6.54
## Dominant
## T/T 42 64.6 39 66.1 1.00 0.8495 138.7
## T/C-C/C 23 35.4 20 33.9 0.91 0.35 2.36
## Recessive
## T/T-T/C 62 95.4 58 98.3 1.00 0.5714 138.4
## C/C 3 4.6 1 1.7 0.49 0.04 6.44
## log-Additive
## 0,1,2 65 52.4 59 47.6 0.86 0.38 1.96 0.7249 138.6
##
## $rs3800373
##
## SNP: rs3800373 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## A/A 30 46.2 27 45.8 1.00 0.7164 140.1
## C/A 33 50.8 27 45.8 0.97 0.38 2.43
## C/C 2 3.1 5 8.5 2.10 0.30 14.71
## Dominant
## A/A 30 46.2 27 45.8 1.00 0.9030 138.7
## C/A-C/C 35 53.8 32 54.2 1.06 0.43 2.59
## Recessive
## A/A-C/A 63 96.9 54 91.5 1.00 0.4161 138.1
## C/C 2 3.1 5 8.5 2.14 0.32 14.08
## log-Additive
## 0,1,2 65 52.4 59 47.6 1.17 0.56 2.45 0.6663 138.6
##
## $rs4570625
##
## SNP: rs4570625 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## G/G 39 60.0 35 59.3 1.00 0.8047 140.3
## G/T 23 35.4 22 37.3 1.38 0.53 3.60
## T/T 3 4.6 2 3.4 1.26 0.14 11.14
## Dominant
## G/G 39 60.0 35 59.3 1.00 0.5125 138.3
## G/T-T/T 26 40.0 24 40.7 1.36 0.54 3.44
## Recessive
## G/G-G/T 62 95.4 57 96.6 1.00 0.9167 138.7
## T/T 3 4.6 2 3.4 1.12 0.13 9.37
## log-Additive
## 0,1,2 65 52.4 59 47.6 1.26 0.58 2.74 0.5573 138.4
##
## $rs53576
##
## SNP: rs53576 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## G/G 23 35.4 22 37.3 1.00 0.04556 134.6
## G/A 36 55.4 25 42.4 0.54 0.20 1.52
## A/A 6 9.2 12 20.3 2.82 0.71 11.13
## Dominant
## G/G 23 35.4 22 37.3 1.00 0.69019 138.6
## G/A-A/A 42 64.6 37 62.7 0.83 0.33 2.10
## Recessive
## G/G-G/A 59 90.8 47 79.7 1.00 0.02845 134.0
## A/A 6 9.2 12 20.3 3.96 1.13 13.93
## log-Additive
## 0,1,2 65 52.4 59 47.6 1.34 0.70 2.59 0.37433 138.0
##
## $rs6265
##
## SNP: rs6265 adjusted by: age gender ctq bdi stai2
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## C/C 42 64.6 44 74.6 1.00 0.5006 139.4
## C/T 21 32.3 13 22.0 0.55 0.20 1.51
## T/T 2 3.1 2 3.4 0.86 0.10 7.73
## Dominant
## C/C 42 64.6 44 74.6 1.00 0.2666 137.5
## C/T-T/T 23 35.4 15 25.4 0.58 0.22 1.53
## Recessive
## C/C-C/T 63 96.9 57 96.6 1.00 0.9803 138.8
## T/T 2 3.1 2 3.4 1.03 0.12 9.04
## log-Additive
## 0,1,2 65 52.4 59 47.6 0.69 0.31 1.53 0.3622 137.9
##
## attr(,"label.SNPs")
## [1] "rs1360780" "rs1491850" "rs1799732" "rs1800532" "rs2254298" "rs3758653"
## [7] "rs3800373" "rs4570625" "rs53576" "rs6265"
## attr(,"models")
## [1] 1 2 3 5
## attr(,"quantitative")
## [1] FALSEOnly rs53576 looks interesting. There might be some signifcances in others, but the allele distribution across the groups is not significant (e.g., when it would be 3 vs. 50).
2.1 Genetic association between genotype and Oxytocin
I run a linear regression analysis with Oxytocin (OT) as dependent variable to see if there is association between genotype and OT. Adding again the covariates on menstrual cycle, menopause and contraception.
## comments codominant dominant recessive
## rs1360780 - 0.24443 0.40117 0.10405
## rs1491850 - 0.44059 0.34579 0.24867
## rs1799732 - 0.85294 - -
## rs1800532 - 0.63537 0.44318 0.41093
## rs2254298 - 0.53858 0.33000 0.46675
## rs3758653 - 0.05724 0.38617 0.01770
## rs3800373 - 0.67376 0.40296 0.62775
## rs4570625 - 0.23332 0.82846 0.08998
## rs53576 - 0.15723 0.08712 0.16703
## rs6265 - 0.89436 0.68671 0.89928##
## SNP: rs3758653 adjusted by: age date_diff menopause contraception ctq bdi stai2
## n me se dif lower upper p-value AIC
## Codominant
## T/T 81 7.250 0.3567 0.000000 0.05966 685.2
## T/C 39 7.603 0.6323 0.240025 -1.1795 1.660
## C/C 4 12.250 3.6291 4.743394 0.8767 8.610
## Dominant
## T/T 81 7.250 0.3567 0.000000 0.39409 688.6
## T/C-C/C 43 8.035 0.6775 0.613494 -0.7921 2.019
## Recessive
## T/T-T/C 120 7.365 0.3154 0.000000 0.01845 683.3
## C/C 4 12.250 3.6291 4.656574 0.8391 8.474
## Overdominant
## T/T-C/C 85 7.486 0.3886 0.000000 0.99045 689.3
## T/C 39 7.603 0.6323 0.008793 -1.4273 1.445
## log-Additive
## 0,1,2 0.940362 -0.2863 2.167 0.13572 686.9We see a significant association with rs3758653. However, when we have a closer look, we see that the results are driven by 4 subjects that are carring the C/C variation. So there results are not biologically significant.
Next we can also do it group-specific:
## [1] "Association between OT and genotypes in FND: "## comments codominant dominant recessive
## rs1360780 - 0.51791 0.33047 0.37956
## rs1491850 - 0.13232 0.05720 0.18140
## rs1799732 - 0.92119 - -
## rs1800532 - 0.99337 0.90880 0.95598
## rs2254298 - 0.76579 0.46282 0.88792
## rs3758653 - 0.98417 0.99755 0.85957
## rs3800373 - 0.54587 0.28900 0.53858
## rs4570625 - 0.78293 0.48940 0.73336
## rs53576 - 0.18370 0.07917 0.21619
## rs6265 - 0.79367 0.64857 0.74784## [1] "Association between OT and genotypes in HC: "## comments codominant dominant recessive
## rs1360780 - 0.78614 0.90786 0.51992
## rs1491850 - 0.83658 0.67501 0.81036
## rs1799732 - 0.18844 - -
## rs1800532 - 0.19029 0.11724 0.14484
## rs2254298 - 0.70211 0.41728 0.66367
## rs3758653 - 0.02698 0.40992 0.00696
## rs3800373 - 0.93278 0.96034 0.70786
## rs4570625 - 0.17051 0.90256 0.06969
## rs53576 - 0.67383 0.37331 0.78290
## rs6265 - 0.44022 0.27674 0.68752There is nothing significant when we split into the two groups (except of rs3758653, which we found to be only 4 subjects). This means that there is no association between genotype and salivary OT. Caveat Later we do an analysis where we split into the three genotypes for rs53576, where we see results.
3 Methylation Data on OXTR
We analysed 5 amplicons of the OXTR receptor. We take the mean of them, which gives us an average methylation rate of the OXTR promoter region. This is standard and has also been done like this in Apazoglou et al.
We again first visualize the data. We first replicate Apazoglou et al., who just run a simple student’s t-test on the mean methylation data. Then we statistically compare them between groups using again the ANOVA on the fitted data.
Covariates used:## [1] FALSE
## [1] "Replication Apazoglou et al.:"##
## Welch Two Sample t-test
##
## data: dfOT$OXTR_CpG_sum[dfOT$group == "FND"] and dfOT$OXTR_CpG_sum[dfOT$group == "HC"]
## t = 1.2951, df = 114.87, p-value = 0.1979
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.004820328 0.023029140
## sample estimates:
## mean of x mean of y
## 0.3977155 0.3886111## [1] "ANOVA on fitted data using Covariates.:"## Df Sum Sq Mean Sq F value Pr(>F)
## OXTR_CpG_sum 1 7.1 7.15 0.546 0.46141
## group 1 10.1 10.11 0.772 0.38147
## psychMed 1 5.8 5.83 0.445 0.50604
## bdi 1 3.0 3.03 0.231 0.63162
## stai1 1 1.2 1.18 0.090 0.76456
## date_diff 1 13.2 13.17 1.006 0.31807
## contraception 2 29.7 14.83 1.133 0.32587
## menopause 1 3.9 3.89 0.297 0.58668
## age 1 4.4 4.40 0.336 0.56319
## OXTR_CpG_sum:group 1 116.7 116.75 8.921 0.00348 **
## Residuals 109 1426.5 13.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingnessFINDINGS: We have a significant OXTR Methylation x Group effect on salivary oxytocin. We want to decouple this effect by looking at the groups individually. We see that the effects of OXTR Methylation goes in the different direction in FND versus HC.
3.1 Interaction between OT, OXTR Methylation and Genotype
As a next step, we investigate some more interaction models. We have the following potential oxytocin related data:
We can make combinations of interactions with all of them. Mostly we find significant results. We just have to think what actually makes sense. Here some of the results:
## [1] "OXTR Methylation ~ Group*genotype with Oxytocin as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 0.00250 0.002503 2.245 0.13700
## rs53576 2 0.02284 0.011422 10.245 8.56e-05 ***
## Oxytocin 1 0.00001 0.000009 0.008 0.92793
## psychMed 1 0.00270 0.002701 2.423 0.12254
## bdi 1 0.00284 0.002844 2.551 0.11323
## stai1 1 0.00027 0.000267 0.239 0.62560
## date_diff 1 0.00004 0.000043 0.039 0.84419
## contraception 2 0.01201 0.006003 5.384 0.00593 **
## menopause 1 0.00897 0.008967 8.043 0.00547 **
## age 1 0.00087 0.000870 0.780 0.37901
## group:rs53576 2 0.00729 0.003643 3.267 0.04197 *
## Residuals 106 0.11818 0.001115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness## [1] FALSE
Particularly, the Genotype seems to have an effect on OXTR
Methylation in FND. Let’s compare it statistically. AA
carriers have higher OXTR Methylation in comparison to GA or GG. While
there are no differences between GA or GG. This effect is not found in
Oxytocin, nor in healthy controls.
## [1] "OXTR Methylation ~ genotype in FND only"## Df Sum Sq Mean Sq F value Pr(>F)
## rs53576 2 0.02837 0.014183 16.310 4.17e-06 ***
## psychMed 1 0.00399 0.003994 4.593 0.0373 *
## bdi 1 0.00337 0.003371 3.877 0.0549 .
## stai1 1 0.00110 0.001104 1.269 0.2656
## date_diff 1 0.00006 0.000064 0.074 0.7867
## contraception 2 0.01153 0.005767 6.632 0.0029 **
## menopause 1 0.00301 0.003008 3.459 0.0692 .
## age 1 0.00142 0.001417 1.629 0.2081
## Residuals 47 0.04087 0.000870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = lm(formula = OXTR_CpG_sum ~ rs53576 + psychMed + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_FND))
##
## $rs53576
## diff lwr upr p adj
## GA-AA -0.049208333 -0.07427130 -0.02414536 0.0000568
## GG-AA -0.058779762 -0.08460528 -0.03295425 0.0000044
## GG-GA -0.009571429 -0.03069618 0.01155332 0.5209513## [1] "Oxytocin ~ genotype in FND only"## Df Sum Sq Mean Sq F value Pr(>F)
## rs53576 2 52.3 26.145 1.762 0.183
## psychMed 1 7.1 7.114 0.480 0.492
## bdi 1 2.1 2.090 0.141 0.709
## stai1 1 2.2 2.168 0.146 0.704
## date_diff 1 8.4 8.411 0.567 0.455
## contraception 2 14.9 7.435 0.501 0.609
## menopause 1 21.1 21.076 1.421 0.239
## age 1 0.5 0.514 0.035 0.853
## Residuals 48 712.1 14.836## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = lm(formula = Oxytocin ~ rs53576 + psychMed + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_FND))
##
## $rs53576
## diff lwr upr p adj
## GA-AA -0.336900 -3.608400 2.9345999 0.9664077
## GG-AA -2.157227 -5.500287 1.1858327 0.2724510
## GG-GA -1.820327 -4.543494 0.9028396 0.2485237## [1] "OXTR Methylation ~ genotype in HC only"## Df Sum Sq Mean Sq F value Pr(>F)
## rs53576 2 0.00219 0.001096 0.837 0.4386
## bdi 1 0.00006 0.000057 0.043 0.8359
## stai1 1 0.00010 0.000100 0.076 0.7833
## date_diff 1 0.00018 0.000177 0.136 0.7142
## contraception 2 0.00365 0.001824 1.394 0.2571
## menopause 1 0.00489 0.004885 3.732 0.0587 .
## age 1 0.00184 0.001843 1.408 0.2407
## Residuals 53 0.06938 0.001309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = lm(formula = OXTR_CpG_sum ~ rs53576 + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_HC))
##
## $rs53576
## diff lwr upr p adj
## GA-AA -0.0009642857 -0.03951329 0.03758472 0.9979963
## GG-AA -0.0131818182 -0.05336302 0.02699938 0.7101315
## GG-GA -0.0122175325 -0.03595437 0.01151931 0.4346302## [1] "Oxytocin ~ genotype in HC only"## Df Sum Sq Mean Sq F value Pr(>F)
## rs53576 2 4.6 2.295 0.166 0.848
## bdi 1 0.0 0.011 0.001 0.977
## stai1 1 4.2 4.228 0.305 0.583
## date_diff 1 11.2 11.159 0.805 0.373
## contraception 2 28.7 14.363 1.036 0.362
## menopause 1 3.5 3.462 0.250 0.619
## age 1 17.4 17.369 1.253 0.268
## Residuals 55 762.3 13.860## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = lm(formula = Oxytocin ~ rs53576 + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_HC))
##
## $rs53576
## diff lwr upr p adj
## GA-AA 0.1773611 -3.776918 4.131640 0.9935862
## GG-AA -0.3937899 -4.504615 3.717035 0.9710913
## GG-GA -0.5711510 -2.964910 1.822608 0.83411754 The effect of Childhood Trauma
As a next step we investigate how childhood trauma might interact with OT, OXTR Methlyation and genotype. I acknowledge that the term “traumatized” versus “no-traumatized” is maybe not optimal, but it refers to the CTQ total score cut off of > 35.
## [1] "Number of traumatized versus non-traumatized:"##
## FND HC
## non-traumatized 24 40
## traumatized 35 25## [1] "Association OT with CTQ stratification, OXTR Methylation and Genotype in FND:"## Df Sum Sq Mean Sq F value Pr(>F)
## group_ctq 1 27.4 27.38 1.998 0.1656
## OXTR_CpG_sum 1 84.2 84.22 6.146 0.0177 *
## rs53576 2 53.7 26.87 1.961 0.1548
## psychMed 1 0.4 0.39 0.029 0.8664
## bdi 1 8.3 8.28 0.604 0.4417
## stai1 1 0.6 0.58 0.042 0.8385
## date_diff 1 11.3 11.30 0.825 0.3695
## contraception 2 14.3 7.17 0.523 0.5969
## menopause 1 1.3 1.34 0.098 0.7562
## age 1 7.3 7.29 0.532 0.4701
## group_ctq:OXTR_CpG_sum 1 1.4 1.40 0.102 0.7514
## group_ctq:rs53576 2 0.6 0.31 0.022 0.9778
## OXTR_CpG_sum:rs53576 2 37.4 18.68 1.363 0.2681
## group_ctq:OXTR_CpG_sum:rs53576 2 10.7 5.36 0.391 0.6788
## Residuals 38 520.7 13.70
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness## [1] "Association OT with CTQ stratification, OXTR Methylation and Genotype in HC:"## Df Sum Sq Mean Sq F value Pr(>F)
## group_ctq 1 3.9 3.91 0.289 0.5937
## OXTR_CpG_sum 1 38.3 38.33 2.828 0.0996 .
## rs53576 2 7.2 3.60 0.266 0.7679
## bdi 1 1.6 1.60 0.118 0.7326
## stai1 1 2.5 2.50 0.184 0.6696
## date_diff 1 9.1 9.07 0.669 0.4176
## contraception 2 53.4 26.72 1.971 0.1511
## menopause 1 0.1 0.06 0.004 0.9469
## age 1 33.6 33.63 2.481 0.1222
## group_ctq:OXTR_CpG_sum 1 9.1 9.09 0.670 0.4172
## group_ctq:rs53576 2 11.9 5.97 0.441 0.6465
## OXTR_CpG_sum:rs53576 2 10.0 4.99 0.368 0.6943
## group_ctq:OXTR_CpG_sum:rs53576 1 39.3 39.30 2.899 0.0955 .
## Residuals 45 609.9 13.55
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingnessResults: We see that not in FND nor in HC there is an association between CTQ stratification group. So no differences in OT, OXTR Methylation or genotype depending on whether you have been CTQ total yes or no. We can look into the individual subscales, just to be sure.
Let’s plot it, independent of genotype. We plot correlations with
each CTQ subtype and OXTR Methylation (left) as well as Oxytocin
(right). However, there are no significant correlations between OXTR
Methylation/Oxytocin and the different childhood traumata.
4.1 CTQ Emotional Neglect
There is no significant association with childhood emotional neglect, nor an interaction effect.
## [1] "Oxytocin ~ Emotional Neglect *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_emoneg 1 10.9 10.858 0.783 0.378
## group 1 7.8 7.768 0.560 0.456
## rs53576 2 56.7 28.339 2.043 0.135
## OXTR_CpG_sum 1 0.1 0.129 0.009 0.923
## psychMed 1 7.8 7.796 0.562 0.455
## bdi 1 0.7 0.711 0.051 0.821
## stai1 1 1.2 1.186 0.085 0.771
## date_diff 1 15.6 15.572 1.122 0.292
## contraception 2 37.0 18.518 1.335 0.268
## menopause 1 1.2 1.175 0.085 0.772
## age 1 6.9 6.864 0.495 0.483
## ctq_emoneg:group 1 5.3 5.279 0.380 0.539
## Residuals 106 1470.6 13.873
## 3 observations deleted due to missingness## [1] "Oxytocin ~ Emotional Neglect *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_emoneg 1 0.00007 0.000073 0.062 0.804186
## group 1 0.00286 0.002857 2.429 0.122068
## rs53576 2 0.02246 0.011232 9.552 0.000153 ***
## Oxytocin 1 0.00001 0.000013 0.011 0.917190
## psychMed 1 0.00293 0.002929 2.490 0.117525
## bdi 1 0.00257 0.002571 2.186 0.142212
## stai1 1 0.00027 0.000267 0.227 0.634745
## date_diff 1 0.00004 0.000041 0.035 0.851911
## contraception 2 0.01206 0.006028 5.126 0.007497 **
## menopause 1 0.00905 0.009054 7.699 0.006531 **
## age 1 0.00081 0.000813 0.691 0.407716
## ctq_emoneg:group 1 0.00073 0.000725 0.617 0.434052
## Residuals 106 0.12465 0.001176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
4.2 CTQ Emotional Abuse
There is no interesting significant association with childhood emotional Abuse, nor an interaction effect. There is a negative correlation in healthy controls between Oxytocin levels and emotional abuse in GG carriers.
## [1] "Oxytocin ~ Emotional Abuse *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_emoab 1 1.9 1.879 0.134 0.715
## group 1 10.7 10.667 0.762 0.385
## rs53576 2 55.1 27.534 1.966 0.145
## OXTR_CpG_sum 1 0.2 0.175 0.012 0.911
## psychMed 1 9.7 9.742 0.695 0.406
## bdi 1 2.3 2.259 0.161 0.689
## stai1 1 1.2 1.232 0.088 0.767
## date_diff 1 14.4 14.403 1.028 0.313
## contraception 2 32.7 16.331 1.166 0.316
## menopause 1 2.9 2.899 0.207 0.650
## age 1 5.6 5.584 0.399 0.529
## ctq_emoab:group 1 0.3 0.349 0.025 0.875
## Residuals 106 1484.7 14.007
## 3 observations deleted due to missingness## [1] "Oxytocin ~ Emotional Abuse *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_emoab 1 0.00196 0.001964 1.716 0.193016
## group 1 0.00360 0.003603 3.149 0.078854 .
## rs53576 2 0.02088 0.010440 9.124 0.000221 ***
## Oxytocin 1 0.00002 0.000017 0.015 0.902886
## psychMed 1 0.00344 0.003439 3.005 0.085897 .
## bdi 1 0.00141 0.001413 1.234 0.269050
## stai1 1 0.00018 0.000180 0.157 0.692449
## date_diff 1 0.00008 0.000079 0.069 0.793773
## contraception 2 0.01215 0.006073 5.307 0.006359 **
## menopause 1 0.00913 0.009134 7.982 0.005646 **
## age 1 0.00082 0.000822 0.719 0.398461
## ctq_emoab:group 1 0.00355 0.003546 3.099 0.081220 .
## Residuals 106 0.12129 0.001144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
4.3 CTQ Physical Neglect
There is no significant association with childhood Physical Neglect, nor an interaction effect.
## [1] "Oxytocin ~ Physical Neglect *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_physneg 1 0.5 0.528 0.038 0.846
## group 1 11.6 11.639 0.832 0.364
## rs53576 2 54.0 26.996 1.930 0.150
## OXTR_CpG_sum 1 0.1 0.137 0.010 0.921
## psychMed 1 10.7 10.698 0.765 0.384
## bdi 1 3.3 3.282 0.235 0.629
## stai1 1 1.3 1.254 0.090 0.765
## date_diff 1 13.9 13.932 0.996 0.321
## contraception 2 32.7 16.337 1.168 0.315
## menopause 1 2.9 2.925 0.209 0.648
## age 1 5.9 5.916 0.423 0.517
## ctq_physneg:group 1 1.8 1.803 0.129 0.720
## Residuals 106 1482.9 13.989
## 3 observations deleted due to missingness## [1] "Oxytocin ~ Physical Neglect *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_physneg 1 0.00310 0.003105 2.700 0.103287
## group 1 0.00324 0.003244 2.822 0.095934 .
## rs53576 2 0.02115 0.010577 9.200 0.000207 ***
## Oxytocin 1 0.00001 0.000013 0.012 0.914485
## psychMed 1 0.00357 0.003574 3.109 0.080756 .
## bdi 1 0.00118 0.001176 1.023 0.314135
## stai1 1 0.00022 0.000217 0.189 0.664872
## date_diff 1 0.00011 0.000110 0.096 0.757188
## contraception 2 0.01147 0.005735 4.988 0.008503 **
## menopause 1 0.00866 0.008656 7.529 0.007131 **
## age 1 0.00070 0.000696 0.605 0.438304
## ctq_physneg:group 1 0.00323 0.003233 2.812 0.096494 .
## Residuals 106 0.12187 0.001150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
4.4 CTQ Physical Abuse
There is no significant association with childhood Physical Abuse, nor an interaction effect.
## [1] "Oxytocin ~ Physical Abuse *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_physab 1 5.2 5.227 0.375 0.542
## group 1 9.2 9.176 0.658 0.419
## rs53576 2 55.2 27.624 1.980 0.143
## OXTR_CpG_sum 1 0.2 0.229 0.016 0.898
## psychMed 1 10.1 10.100 0.724 0.397
## bdi 1 1.3 1.287 0.092 0.762
## stai1 1 1.0 1.005 0.072 0.789
## date_diff 1 15.0 15.035 1.078 0.302
## contraception 2 34.3 17.129 1.228 0.297
## menopause 1 3.5 3.504 0.251 0.617
## age 1 5.2 5.246 0.376 0.541
## ctq_physab:group 1 2.8 2.760 0.198 0.657
## Residuals 106 1478.6 13.949
## 3 observations deleted due to missingness## [1] "Oxytocin ~ Physical Abuse *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_physab 1 0.00066 0.000657 0.557 0.45696
## group 1 0.00330 0.003301 2.800 0.09721 .
## rs53576 2 0.02259 0.011294 9.581 0.00015 ***
## Oxytocin 1 0.00002 0.000022 0.019 0.89051
## psychMed 1 0.00293 0.002933 2.488 0.11769
## bdi 1 0.00181 0.001805 1.532 0.21862
## stai1 1 0.00022 0.000220 0.187 0.66654
## date_diff 1 0.00006 0.000063 0.053 0.81798
## contraception 2 0.01174 0.005869 4.979 0.00858 **
## menopause 1 0.00886 0.008860 7.516 0.00718 **
## age 1 0.00087 0.000874 0.741 0.39113
## ctq_physab:group 1 0.00050 0.000505 0.428 0.51433
## Residuals 106 0.12495 0.001179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
4.5 CTQ Sexual Abuse
There is no significant association with childhood Sexual Neglect, nor an interaction effect.
## [1] "Oxytocin ~ Sexual Abuse *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_sexab 1 5.4 5.449 0.390 0.534
## group 1 11.1 11.149 0.798 0.374
## rs53576 2 54.4 27.223 1.948 0.148
## OXTR_CpG_sum 1 0.1 0.069 0.005 0.944
## psychMed 1 10.7 10.741 0.769 0.383
## bdi 1 0.8 0.833 0.060 0.808
## stai1 1 1.1 1.094 0.078 0.780
## date_diff 1 15.2 15.198 1.088 0.299
## contraception 2 31.6 15.820 1.132 0.326
## menopause 1 2.8 2.826 0.202 0.654
## age 1 7.0 7.033 0.503 0.480
## ctq_sexab:group 1 0.0 0.043 0.003 0.956
## Residuals 106 1481.1 13.973
## 3 observations deleted due to missingness## [1] "Oxytocin ~ Sexual Abuse *group + with genotype and methlyation as covariate"## Df Sum Sq Mean Sq F value Pr(>F)
## ctq_sexab 1 0.00006 0.000055 0.047 0.82857
## group 1 0.00247 0.002468 2.102 0.15003
## rs53576 2 0.02288 0.011438 9.745 0.00013 ***
## Oxytocin 1 0.00001 0.000007 0.006 0.93934
## psychMed 1 0.00269 0.002690 2.292 0.13303
## bdi 1 0.00357 0.003575 3.046 0.08386 .
## stai1 1 0.00029 0.000288 0.245 0.62133
## date_diff 1 0.00002 0.000023 0.019 0.88991
## contraception 2 0.01177 0.005883 5.012 0.00832 **
## menopause 1 0.00886 0.008864 7.552 0.00705 **
## age 1 0.00110 0.001100 0.937 0.33513
## ctq_sexab:group 1 0.00039 0.000390 0.332 0.56546
## Residuals 106 0.12442 0.001174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
5 Oxytocin, OXTR Methylation, Genotype and their relation to Brain Volume.
For the genetics project, we have already prepared the brain volumes that are significantly associated with the genotype in FND. Here the table as a summary:
Let’s have a look at Oxytocin and the brain volume
of some specific ROIs. I just make here really rough models where I
include a Oxytocin x OXTR Methylation x Genotype tripple interaciton. We
can disentangle this together in the next meeting if needed, but I think
it would be nore interessting to focus on fMRI.
## [1] "Amygdala Brain Volume in FND: "
## [1] "Amygdala Brain Volume in HC: "## Df Sum Sq Mean Sq F value Pr(>F)
## Oxytocin 1 0.0142 0.0142 0.436 0.51269
## OXTR_CpG_sum 1 0.2623 0.2623 8.068 0.00705 **
## rs53576 2 0.0180 0.0090 0.277 0.75932
## TIV 1 0.6894 0.6894 21.204 4.13e-05 ***
## gender 1 0.7222 0.7222 22.213 2.94e-05 ***
## bdi 1 0.0222 0.0222 0.682 0.41378
## stai2 1 0.0511 0.0511 1.573 0.21710
## date_diff 1 0.0127 0.0127 0.392 0.53473
## contraception 2 0.0693 0.0347 1.066 0.35384
## age 1 0.3252 0.3252 10.003 0.00298 **
## ctq 1 0.0021 0.0021 0.065 0.80075
## Oxytocin:OXTR_CpG_sum 1 0.0175 0.0175 0.537 0.46781
## Oxytocin:rs53576 2 0.0472 0.0236 0.725 0.49052
## OXTR_CpG_sum:rs53576 2 0.0029 0.0014 0.044 0.95686
## Oxytocin:OXTR_CpG_sum:rs53576 2 0.0831 0.0416 1.279 0.28953
## Residuals 40 1.3005 0.0325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness## [1] "Hippocampus Brain Volume in FND: "## Df Sum Sq Mean Sq F value Pr(>F)
## Oxytocin 1 1.136 1.1357 2.291 0.1388
## OXTR_CpG_sum 1 0.007 0.0069 0.014 0.9067
## rs53576 2 0.387 0.1933 0.390 0.6799
## TIV 1 2.989 2.9893 6.031 0.0190 *
## gender 1 0.636 0.6360 1.283 0.2648
## bdi 1 0.000 0.0002 0.000 0.9833
## stai2 1 0.028 0.0276 0.056 0.8149
## date_diff 1 1.544 1.5443 3.116 0.0860 .
## contraception 2 0.564 0.2819 0.569 0.5712
## age 1 0.831 0.8312 1.677 0.2036
## ctq 1 0.065 0.0651 0.131 0.7192
## Oxytocin:OXTR_CpG_sum 1 2.638 2.6376 5.322 0.0269 *
## Oxytocin:rs53576 2 0.208 0.1039 0.210 0.8119
## OXTR_CpG_sum:rs53576 2 0.016 0.0082 0.017 0.9836
## Oxytocin:OXTR_CpG_sum:rs53576 2 0.221 0.1107 0.223 0.8009
## Residuals 36 17.842 0.4956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness## [1] "Hippocampus Brain Volume in HC: "## Df Sum Sq Mean Sq F value Pr(>F)
## Oxytocin 1 0.355 0.355 1.000 0.32334
## OXTR_CpG_sum 1 1.411 1.411 3.969 0.05320 .
## rs53576 2 1.575 0.787 2.215 0.12235
## TIV 1 4.200 4.200 11.817 0.00138 **
## gender 1 1.966 1.966 5.531 0.02369 *
## bdi 1 0.069 0.069 0.194 0.66172
## stai2 1 0.609 0.609 1.715 0.19786
## date_diff 1 0.783 0.783 2.203 0.14555
## contraception 2 0.457 0.229 0.643 0.53083
## age 1 2.527 2.527 7.111 0.01100 *
## ctq 1 0.103 0.103 0.290 0.59316
## Oxytocin:OXTR_CpG_sum 1 0.555 0.555 1.562 0.21862
## Oxytocin:rs53576 2 2.889 1.445 4.065 0.02472 *
## OXTR_CpG_sum:rs53576 2 0.456 0.228 0.641 0.53208
## Oxytocin:OXTR_CpG_sum:rs53576 2 0.168 0.084 0.236 0.79056
## Residuals 40 14.216 0.355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness## [1] "Insula Brain Volume in FND: "## Df Sum Sq Mean Sq F value Pr(>F)
## Oxytocin 1 0.00 0.00 0.001 0.969625
## OXTR_CpG_sum 1 3.57 3.57 1.408 0.243184
## rs53576 2 0.82 0.41 0.161 0.851685
## TIV 1 42.30 42.30 16.683 0.000235 ***
## gender 1 0.04 0.04 0.016 0.900559
## bdi 1 2.27 2.27 0.894 0.350734
## stai2 1 1.87 1.87 0.736 0.396662
## date_diff 1 4.58 4.58 1.807 0.187228
## contraception 2 9.81 4.91 1.936 0.159055
## age 1 12.63 12.63 4.982 0.031932 *
## ctq 1 1.34 1.34 0.528 0.472257
## Oxytocin:OXTR_CpG_sum 1 2.03 2.03 0.800 0.376996
## Oxytocin:rs53576 2 0.56 0.28 0.110 0.896160
## OXTR_CpG_sum:rs53576 2 4.74 2.37 0.935 0.401874
## Oxytocin:OXTR_CpG_sum:rs53576 2 5.66 2.83 1.117 0.338389
## Residuals 36 91.27 2.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness## [1] "Insula Brain Volume in HC: "## Df Sum Sq Mean Sq F value Pr(>F)
## Oxytocin 1 5.75 5.749 4.101 0.049567 *
## OXTR_CpG_sum 1 3.02 3.018 2.153 0.150133
## rs53576 2 3.12 1.561 1.113 0.338440
## TIV 1 13.01 13.014 9.283 0.004083 **
## gender 1 18.05 18.046 12.873 0.000899 ***
## bdi 1 2.36 2.362 1.685 0.201750
## stai2 1 0.24 0.236 0.168 0.683751
## date_diff 1 10.98 10.978 7.831 0.007863 **
## contraception 2 6.00 3.000 2.140 0.130904
## age 1 16.33 16.335 11.652 0.001481 **
## ctq 1 0.23 0.233 0.166 0.685719
## Oxytocin:OXTR_CpG_sum 1 0.24 0.235 0.168 0.684154
## Oxytocin:rs53576 2 4.32 2.158 1.540 0.226919
## OXTR_CpG_sum:rs53576 2 1.44 0.721 0.514 0.601792
## Oxytocin:OXTR_CpG_sum:rs53576 2 2.41 1.207 0.861 0.430323
## Residuals 40 56.07 1.402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness6 Oxytocin and functional connectivity in FND
What have I done? I calculated functional connectivity according to standard procedures. This gives me - using the AAL atlas 4005 individual brain connections. I then calculate their correlation with Oxytocin connectionwise. This gives me a 90x90 correlation matrix at which the “blobs” show those connections that significantly correlate with OT.
Correlations in HC (left) and FND (right) with a significance level of alpha = 0.01. I am a bit more strict, as we have many connections, FDR however does not need to be applied as these are correlations and not a statistical test.
We now further investigate the significant correlations in FND only.
The following connections significantly correlate
with Oxytocin in FND:
Session info
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] viridis_0.6.4 viridisLite_0.4.1 ggtext_0.1.2 mediation_4.5.0
## [5] sandwich_3.0-2 corrplot_0.92 SNPassoc_2.0-17 forcats_0.5.2
## [9] stringr_1.5.0 purrr_1.0.1 readr_2.1.3 tidyr_1.2.1
## [13] tibble_3.2.1 tidyverse_1.3.2 dplyr_1.1.2 multcomp_1.4-20
## [17] TH.data_1.1-1 survival_3.4-0 mvtnorm_1.1-3 plyr_1.8.8
## [21] lme4_1.1-30 Matrix_1.5-1 rstatix_0.7.1 ggpubr_0.5.0
## [25] lawstat_3.5 car_3.1-2 carData_3.0-5 readxl_1.4.1
## [29] plotly_4.10.1 Hmisc_5.0-1 ggplot2_3.4.2 MASS_7.3-58.1
## [33] ISwR_2.0-8
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2
## [2] rms_6.3-0
## [3] tidyselect_1.2.0
## [4] RSQLite_2.2.18
## [5] AnnotationDbi_1.58.0
## [6] htmlwidgets_1.6.4
## [7] grid_4.2.1
## [8] BiocParallel_1.30.4
## [9] lpSolve_5.6.18
## [10] munsell_0.5.0
## [11] codetools_0.2-18
## [12] withr_2.5.0
## [13] colorspace_2.1-0
## [14] Biobase_2.56.0
## [15] filelock_1.0.2
## [16] highr_0.9
## [17] knitr_1.40
## [18] rstudioapi_0.14
## [19] stats4_4.2.1
## [20] ggsignif_0.6.4
## [21] labeling_0.4.2
## [22] MatrixGenerics_1.8.1
## [23] Rdpack_2.4
## [24] GenomeInfoDbData_1.2.8
## [25] farver_2.1.1
## [26] bit64_4.0.5
## [27] vctrs_0.6.2
## [28] generics_0.1.3
## [29] xfun_0.37
## [30] timechange_0.1.1
## [31] BiocFileCache_2.4.0
## [32] markdown_1.5
## [33] R6_2.5.1
## [34] GenomeInfoDb_1.32.4
## [35] Kendall_2.2.1
## [36] bitops_1.0-7
## [37] cachem_1.0.6
## [38] DelayedArray_0.22.0
## [39] assertthat_0.2.1
## [40] BiocIO_1.6.0
## [41] scales_1.2.1
## [42] nnet_7.3-18
## [43] googlesheets4_1.0.1
## [44] gtable_0.3.1
## [45] rlang_1.1.0
## [46] MatrixModels_0.5-1
## [47] splines_4.2.1
## [48] rtracklayer_1.56.1
## [49] lazyeval_0.2.2
## [50] gargle_1.2.1
## [51] broom_1.0.1
## [52] checkmate_2.1.0
## [53] BiocManager_1.30.18
## [54] yaml_2.3.6
## [55] abind_1.4-5
## [56] modelr_0.1.9
## [57] GenomicFeatures_1.48.4
## [58] backports_1.4.1
## [59] gridtext_0.1.5
## [60] tools_4.2.1
## [61] ellipsis_0.3.2
## [62] jquerylib_0.1.4
## [63] BiocGenerics_0.42.0
## [64] Rcpp_1.0.9
## [65] base64enc_0.1-3
## [66] progress_1.2.2
## [67] zlibbioc_1.42.0
## [68] poisbinom_1.0.1
## [69] RCurl_1.98-1.9
## [70] prettyunits_1.1.1
## [71] rpart_4.1.19
## [72] cowplot_1.1.1
## [73] S4Vectors_0.34.0
## [74] zoo_1.8-11
## [75] SummarizedExperiment_1.26.1
## [76] haven_2.5.1
## [77] cluster_2.1.4
## [78] fs_1.5.2
## [79] magrittr_2.0.3
## [80] data.table_1.14.6
## [81] SparseM_1.81
## [82] reprex_2.0.2
## [83] googledrive_2.0.0
## [84] matrixStats_0.62.0
## [85] hms_1.1.2
## [86] evaluate_0.17
## [87] XML_3.99-0.11
## [88] IRanges_2.30.1
## [89] gridExtra_2.3
## [90] compiler_4.2.1
## [91] biomaRt_2.52.0
## [92] crayon_1.5.2
## [93] arsenal_3.6.3
## [94] minqa_1.2.5
## [95] htmltools_0.5.7
## [96] mgcv_1.8-41
## [97] tzdb_0.3.0
## [98] Formula_1.2-4
## [99] lubridate_1.9.0
## [100] DBI_1.1.3
## [101] dbplyr_2.2.1
## [102] rappdirs_0.3.3
## [103] boot_1.3-28
## [104] BiocStyle_2.24.0
## [105] cli_3.4.1
## [106] rbibutils_2.2.13
## [107] parallel_4.2.1
## [108] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [109] GenomicRanges_1.48.0
## [110] pkgconfig_2.0.3
## [111] GenomicAlignments_1.32.1
## [112] haplo.stats_1.8.9
## [113] foreign_0.8-83
## [114] xml2_1.3.3
## [115] bslib_0.4.0
## [116] XVector_0.36.0
## [117] rvest_1.0.3
## [118] VariantAnnotation_1.42.1
## [119] digest_0.6.30
## [120] Biostrings_2.64.1
## [121] rmarkdown_2.20
## [122] cellranger_1.1.0
## [123] htmlTable_2.4.1
## [124] restfulr_0.0.15
## [125] curl_4.3.3
## [126] commonmark_1.8.1
## [127] Rsamtools_2.12.0
## [128] quantreg_5.94
## [129] rjson_0.2.21
## [130] nloptr_2.0.3
## [131] lifecycle_1.0.3
## [132] nlme_3.1-160
## [133] jsonlite_1.8.3
## [134] BSgenome_1.64.0
## [135] fansi_1.0.3
## [136] pillar_1.9.0
## [137] lattice_0.20-45
## [138] KEGGREST_1.36.3
## [139] fastmap_1.1.0
## [140] httr_1.4.4
## [141] glue_1.6.2
## [142] png_0.1-7
## [143] bit_4.0.4
## [144] stringi_1.7.8
## [145] sass_0.4.2
## [146] blob_1.2.3
## [147] polspline_1.1.20
## [148] org.Hs.eg.db_3.15.0
## [149] memoise_2.0.1